{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Social_Network_Ads.csv')\n",
    "X = dataset.iloc[:, [2, 3]].values\n",
    "y = dataset.iloc[:, 4].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# Feature Scaling\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63  5]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Fitting classifier to the Training set\n",
    "# Create your classifier here\n",
    "classifier = RandomForestClassifier(n_estimators=10,criterion='entropy',random_state=0)\n",
    "classifier.fit(X_train,y_train)\n",
    "# Predicting the Test set results\n",
    "y_pred = classifier.predict(X_test)\n",
    "\n",
    "# Making the Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UfCOTG/JOphvTROQaYAlwhIg8BnxCVeNWAjBGITaLGx0Eraai9LAOc24U91JD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\nF2GyjN4FfApvlTCI1z1P8X7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8y01TkAKqGuhKagTWYGdk2Aohb9RTvKVZbhJlqks0ajUS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\nAfyzKuSV3NT5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14150b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ewFBMCyUF4gSEH5jZN4DpZvYu4B3ApekmqwlUaiStpf46r4Embu8pSGdsQRVt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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14717ff0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the Training set results\n",
    "X_set, y_set = X_train, y_train\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Random Forest Classifier (Training set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Visualising the Test set results\n",
    "X_set, y_set = X_test, y_test\n",
    "X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
    "                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
    "plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
    "             alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
    "plt.xlim(X1.min(), X1.max())\n",
    "plt.ylim(X2.min(), X2.max())\n",
    "for i, j in enumerate(np.unique(y_set)):\n",
    "    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
    "                c = ListedColormap(('red', 'green'))(i), label = j)\n",
    "plt.title('Random Forest Classifier (Test set)')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Estimated Salary')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
